This project presents the development of an automated system for monitoring plant health, integrating computer vision and AI to detect leaf diseases and assess environmental conditions, particularly soil moisture. Designed for urban and precision agriculture, the system supports sustainability by optimizing water usage and enabling early disease detection. It combines convolutional neural networks (CNNs), Raspberry Pi image capture, and ESP32-based soil moisture sensors. The result is a self-contained solution that captures data, classifies plant health, and provides actionable irrigation and treatment recommendations.
